Zobrazeno 1 - 10
of 207
pro vyhledávání: '"Gibou, Frederic"'
Autor:
Tomlinson, Samuel D., Gibou, Frédéric, Luzzatto-Fegiz, Paolo, Temprano-Coleto, Fernando, Jensen, Oliver E., Landel, Julien R.
Motivated by microfluidic applications, we investigate drag reduction in laminar pressure-driven flows in channels with streamwise-periodic superhydrophobic surfaces (SHSs) that are contaminated with soluble surfactant. We develop a model in the long
Externí odkaz:
http://arxiv.org/abs/2406.15251
Autor:
Mcnair, Richard, Temprano-Coleto, Fernando, Peaudecerf, François J., Gibou, Frédéric, Luzzatto-Fegiz, Paolo, Jensen, Oliver E., Landel, Julien R.
Experiments have shown that surfactant introduced to a liquid-filled maze can find the solution path. We reveal how the maze-solving dynamics arise from interactions between the added surfactant and endogenous surfactant present at the liquid surface
Externí odkaz:
http://arxiv.org/abs/2311.08133
Autor:
Tomlinson, Samuel D., Gibou, Frédéric, Luzzatto-Fegiz, Paolo, Temprano-Coleto, Fernando, Jensen, Oliver E., Landel, Julien R.
Publikováno v:
J. Fluid Mech. 1000 (2024) A62
Recognising that surfactants can impede the drag reduction resulting from superhydrophobic surfaces (SHSs), we investigate the impact of spatio-temporal fluctuations in surfactant concentration on the drag-reduction properties of SHSs. We model the u
Externí odkaz:
http://arxiv.org/abs/2310.18184
Autor:
Tomlinson, Samuel D., Peaudecerf, François, Temprano-Coleto, Fernando, Gibou, Frederic, Luzzatto-Fegiz, Paolo, Jensen, Oliver E., Landel, Julien R.
Superhydrophobic surfaces (SHSs) can reduce the friction drag in turbulent flows. In the laminar regime, it has been shown that trace amounts of surfactant can negate this drag reduction, at times rendering these surfaces no better than solid walls (
Externí odkaz:
http://arxiv.org/abs/2302.01634
We present a scalable strategy for development of mesh-free hybrid neuro-symbolic partial differential equation solvers based on existing mesh-based numerical discretization methods. Particularly, this strategy can be used to efficiently train neural
Externí odkaz:
http://arxiv.org/abs/2210.14312
We present a highly scalable strategy for developing mesh-free neuro-symbolic partial differential equation solvers from existing numerical discretizations found in scientific computing. This strategy is unique in that it can be used to efficiently t
Externí odkaz:
http://arxiv.org/abs/2210.14907
We present a numerical method for the solution of interfacial growth governed by the Stefan model coupled with incompressible fluid flow. An algorithm is presented which takes special care to enforce sharp interfacial conditions on the temperature, t
Externí odkaz:
http://arxiv.org/abs/2209.13863
Autor:
Tomlinson, Samuel D., Gibou, Frédéric, Luzzatto-Fegiz, Paolo, Temprano-Coleto, Fernando, Jensen, Oliver E., Landel, Julien R.
While superhydrophobic surfaces (SHSs) show promise for drag reduction applications, their performance can be compromised by traces of surfactant, which generate Marangoni stresses that increase drag. This question is addressed for soluble surfactant
Externí odkaz:
http://arxiv.org/abs/2209.04834
Machine learning algorithms for three-dimensional mean-curvature computation in the level-set method
We propose a data-driven mean-curvature solver for the level-set method. This work is the natural extension to $\mathbb{R}^3$ of our two-dimensional strategy in [DOI: 10.1007/s10915-022-01952-2][1] and the hybrid inference system of [DOI: 10.1016/j.j
Externí odkaz:
http://arxiv.org/abs/2208.09047
Publikováno v:
J. Sci. Comput., 93(1):6, October 2022
We present an error-neural-modeling-based strategy for approximating two-dimensional curvature in the level-set method. Our main contribution is a redesigned hybrid solver [Larios-C\'ardenas and Gibou, J. Comput. Phys. (May 2022), 10.1016/j.jcp.2022.
Externí odkaz:
http://arxiv.org/abs/2201.12342